Abstract: Using index file is a new way of solving the large amount of RDF (Resource Description Framework) query problem,which can be a great aid to query optimization.At present,most of the RDF query optimization method based on Hadoop do not use index file,and most of them aim at static data so they perform poorly at dynamic updating of data.In order to overcome these two drawbacks,this paper proposed IMSQ (using Index in MapReduce to Segment and Query) algorithm to perform distributed RDF query.The algorithm can be divided into segment and query execution two parts,firstly,makes a starlike segmentation for RDF data,and obtaines several segment file and corresponding index file,secondly,generates a layered join plan,uses filter method to seek the index file to narrow the result set and then does query on corresponding segment file；finally,merges and outputs the middle result. The results of the experiment on the LUBM test data set show that IMSQ method query efficiency is higher when the amount of the RDF data is large.

Key words: Hadoop,RDF,Index,MapReduce